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RStudio is an integrated development environment (IDE) for R. RStudio is available in open source and commercial editions and runs on the desktop (Windows, Mac, and Linux).

R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. R is available as Free Software.

• Determine the libraries to use
• Read the file and transform the data
• Select the more appropriate kind of chart to represent the information
• Create a dynamic time series chart

R packages are a collection of R functions, compiled code and sample data. They are stored under a directory called “library” in the R environment. By default, R installs a set of packages during installation. More packages are added later when they are needed for some specific purpose.

library(readxl) #Read the data from excel sheet
library(DT) #Show a dynamic table
library(ggplot2) #Create Charts
library(xts) #Create Time Series
library(highcharter) #Represent the data in timeseries chart
library(imager) #Open image

#Load the table, establishing the sheet, range and column types of the data

Turtle <- read_excel("Turtles.xlsx", 
                     sheet = "Chart", range = cell_cols("A:C"), 
                     col_types = c("date","numeric", "numeric")) 

#Change column names

names(Turtle)[2] <- "egglaying"
names(Turtle)[3] <- "nharv"

table <- datatable(Turtle, extensions = c('Buttons','FixedColumns', 'RowReorder'),
  filter = list(position = 'top', clear = FALSE),
  options = list(dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print'), dom = 't',
    scrollX = TRUE,
    fixedColumns = TRUE,
    rowReorder = TRUE, order = list(c(0 , 'asc'))))

summary(Turtle[,2:3])

##    egglaying          nharv        
##  Min.   :  2044   Min.   :  4.042  
##  1st Qu.: 13544   1st Qu.:  8.735  
##  Median : 37309   Median : 15.709  
##  Mean   : 65980   Mean   : 21.406  
##  3rd Qu.: 74106   3rd Qu.: 28.998  
##  Max.   :477342   Max.   :100.000

#Create chart using ggplot tools

Chart_1 <- Turtle %>% ggplot() + 
  geom_bar(mapping = aes(x = as.Date(Date, "%Y-%m", tz = "UTC"), y = egglaying, group = 2, 
            size = "Eggs Laying"),  stat = "identity", colour = "#2F528F", fill = "#2F528F") +
  scale_size_manual(NULL, values = 0.5) +
  geom_point(mapping = aes(x = as.Date(Date, "%Y-%m", tz = "UTC"), 
            y = nharv * 500000/100, group = 1, color = "Nest harvested"), size = 3, 
            shape = 21, fill = "#ED7D31", inherit.aes = FALSE) +
  geom_line(mapping = aes(x = as.Date(Date, "%Y-%m", tz = "UTC"), 
            y = nharv * 500000/100, group = 1, color = "Nest harvested"), colour="#ED7D31", 
            inherit.aes = FALSE) +  scale_color_manual(NULL, values = "#ED7D31") +
  scale_x_date(date_labels = "%b-%Y", date_breaks = "6 months") +
  scale_y_continuous(name = "Egg Laying Females (total number)", 
    sec.axis = sec_axis(~. * 100/500000, name="Nests Harvested (percent)"),
    limits = c(0, 500000)) +  xlab("Date") + theme_bw() +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
    legend.position="top", legend.background = element_rect(fill = "transparent"), 
    legend.box.background = element_rect(fill = "transparent", colour = NA),
    legend.key = element_rect(fill = "transparent"), legend.spacing = unit(-1, "lines"),
    plot.title = element_text(hjust = 0.5, size = 14), 
    plot.subtitle = element_text(hjust = 0.5, size = 10),
    plot.caption = element_text(hjust = 0, face = "italic")) +
  labs(title = "Total number of Egg Laying Females and Percent of Nests Harvested
       Ostional - Costa Rica, Period 2006-2010",
       subtitle = "Figure adapted from Valverde et al. 2012",
       caption ="By: Jose Francisco Nuñez O")

A time series chart, also called a times series graph or time series plot, is a data visualization tool that illustrates data points at successive intervals of time. Each point on the chart corresponds to both a time and a quantity that is being measured.

# Change dataset to Time Series format

Turtle.ts.eggs <- xts(Turtle$egglaying, Turtle$Date)
Turtle.ts.nest <- xts(Turtle$nharv, Turtle$Date)

#Create the Time Series Chart

Chart_2 <- highchart(type = "stock") %>% 
  hc_title(text = "Total number of Egg Laying Females and Percent of Nests Harvested
       <br>Ostional - Costa Rica, Period 2006-2010</br>") %>% 
  hc_subtitle(text = "Figure Adapted from: Valverde et al. 2012") %>%
  hc_yAxis_multiples(list(title = list(text = "Egg Laying Females (total number)"), opposite = FALSE),
      list(showLastLabel = FALSE, opposite = TRUE, title = list(text = "Nests Harvested (percent)"))) %>% 
  hc_add_series(Turtle.ts.eggs,  yAxis = 0, type = "column", 
      pointInterval= 30*24*3600*1000, name="Egg Laying Females", color = "#2F528F")%>%
  hc_add_series(Turtle.ts.nest,  yAxis = 1, type = "spline", 
      pointInterval= 30*24*3600*1000, name="Percentage of Nests Harvested", color = "#ED7D31")%>%
  hc_plotOptions(spline = list(marker = list(enabled = TRUE, radius = 4))) %>% 
  hc_xAxis(title= list(text='Date'), type='datetime') %>% 
  hc_exporting(enabled = TRUE, filename = "Eggs_Nests_Turtle_Ostional") %>% 
  hc_credits(enabled = TRUE, text = "By: Jose Francisco Nuñez O", position=list(align = "center"),
             href = "http://geoimaginarte.github.io/mysite") %>% 
  hc_legend(enabled=TRUE)

im <- load.image("chart2.jpg")
im %>% plot(axes=F)